Three-dimensional (3D) models (e.g., 3D representations of building spaces) are often used in a number of architectural and engineering applications. As 3D models for a particular space are often not available, 3D models must be newly generated for each of these spaces. In some cases, this involves the use of a drafter, who models the space by manually using a computer aided drafting (CAD) application. A number of automated systems are also available that use laser scanners or other sensors for acquisition of 3D data. However, these systems often collect point-cloud data which includes an unnecessarily large number of data points, making these systems memory intensive and inefficient.
Systems for generating 3D models of indoor spaces face a number of additional technical challenges. For example, these systems are often unable to distinguish the space from objects within that space. In some cases, users of a system may be forced to remove objects from the space before modeling the space to obtain an accurate model. Some systems may be capable of automatically extrapolating out point cloud data to estimate the bounds of a space. However, these systems often just identify the most likely candidate for each structural feature (e.g., walls, floors, and ceilings) of the space and generate a 3D model of the space from those likely structural features. This often results in the system disregarding atypical structural features of a space as “clutter,” and results in generation of a 3D model that lacks those atypical structural features. As a result, these systems are usually only able to generate 3D models of conventional spaces, making the systems unusable for a number of spaces.
Embodiments of the invention address these and other problems, individually and collectively.